Hand-Assisted Laparoscopic Surgery (HALS) With the HandPort System
Bibliographic record
Abstract
OBJECTIVE: To evaluate the feasibility and potential benefits of hand-assisted laparoscopic surgery with the HandPort System, a new device. SUMMARY BACKGROUND DATA: In hand-assisted laparoscopic surgery, the surgeon inserts a hand into the abdomen while pneumoperitoneum is maintained. The hand assists laparoscopic instruments and is helpful in complex laparoscopic cases. METHODS: A prospective nonrandomized study was initiated with the participation of 10 laparoscopic surgical centers. Surgeons were free to test the device in any situation where they expected a potential advantage over conventional laparoscopy. RESULTS: Sixty-eight patients were entered in the study. Operations included colorectal procedures (sigmoidectomy, right colectomy, resection rectopexy), splenectomy for splenomegaly, living-related donor nephrectomy, gastric banding for morbid obesity, partial gastrectomy, and various other procedures. Mean incision size for the HandPort was 7.4 cm. Most surgeons (78%) preferred to insert their nondominant hand into the abdomen. Pneumoperitoneum was generally maintained at 14 mmHg, and only one patient required conversion to open surgery as a result of an unmanageable air leak. Hand fatigue during surgery was noted in 20.6%. CONCLUSIONS: The hand-assisted technique appeared to be useful in minimally invasive colorectal surgery, splenectomy for splenomegaly, living-related donor nephrectomy, and procedures considered too complex for a laparoscopic approach. This approach provides excellent means to explore, to retract safely, and to apply immediate hemostasis when needed. Although the data presented here reflect the authors' initial experience, they compare favorably with series of similar procedures performed purely laparoscopically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".